






"""simple docstring"""


import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[Any]      =  """MCTCTFeatureExtractor"""
					lowercase_    :				Dict      =  """AutoTokenizer"""
					def __init__(							self							:	Optional[Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	List[str]   )	->       Optional[int]:
											super().__init__(UpperCAmelCase     , UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			self.feature_extractor
											lowerCAmelCase   :Dict						=			False
					def __call__(							self							:	List[Any]     , *UpperCAmelCase							:	Tuple     , **UpperCAmelCase							:	Optional[Any]   )	->       str:
											# For backward compatibility
											if self._in_target_context_manager:
																	return self.current_processor(*UpperCAmelCase     , **UpperCAmelCase   )

											if "raw_speech" in kwargs:
																	warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
																	lowerCAmelCase   :Any						=			kwargs.pop('raw_speech'   )
											else:
																	lowerCAmelCase   :Optional[int]						=			kwargs.pop('audio'     , UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			kwargs.pop('sampling_rate'     , UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			kwargs.pop('text'     , UpperCAmelCase   )
											if len(UpperCAmelCase   ) > 0:
																	lowerCAmelCase   :str						=			args[0]
																	lowerCAmelCase   :List[str]						=			args[1:]

											if audio is None and text is None:
																	raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

											if audio is not None:
																	lowerCAmelCase   :Tuple						=			self.feature_extractor(UpperCAmelCase     , *UpperCAmelCase     , sampling_rate=UpperCAmelCase     , **UpperCAmelCase   )
											if text is not None:
																	lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , **UpperCAmelCase   )

											if text is None:
																	return inputs
											elif audio is None:
																	return encodings
											else:
																	lowerCAmelCase   :int						=			encodings['input_ids']
																	return inputs
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , *UpperCAmelCase							:	Optional[Any]     , **UpperCAmelCase							:	Optional[int]   )	->       Optional[int]:
											return self.tokenizer.batch_decode(*UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[int]     , *UpperCAmelCase							:	Tuple     , **UpperCAmelCase							:	Union[str, Any]   )	->       Union[str, Any]:
											# For backward compatibility
											if self._in_target_context_manager:
																	return self.current_processor.pad(*UpperCAmelCase     , **UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			kwargs.pop('input_features'     , UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			kwargs.pop('labels'     , UpperCAmelCase   )
											if len(UpperCAmelCase   ) > 0:
																	lowerCAmelCase   :List[str]						=			args[0]
																	lowerCAmelCase   :Any						=			args[1:]

											if input_features is not None:
																	lowerCAmelCase   :Optional[Any]						=			self.feature_extractor.pad(UpperCAmelCase     , *UpperCAmelCase     , **UpperCAmelCase   )
											if labels is not None:
																	lowerCAmelCase   :int						=			self.tokenizer.pad(UpperCAmelCase     , **UpperCAmelCase   )

											if labels is None:
																	return input_features
											elif input_features is None:
																	return labels
											else:
																	lowerCAmelCase   :List[Any]						=			labels['input_ids']
																	return input_features
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , *UpperCAmelCase							:	List[Any]     , **UpperCAmelCase							:	Union[str, Any]   )	->       Optional[Any]:
											return self.tokenizer.decode(*UpperCAmelCase     , **UpperCAmelCase   )
					@contextmanager
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[Any]:
											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your audio inputs, or in a separate call.'   )
											lowerCAmelCase   :int						=			True
											lowerCAmelCase   :Optional[Any]						=			self.tokenizer
											yield
											lowerCAmelCase   :List[Any]						=			self.feature_extractor
											lowerCAmelCase   :Any						=			False







"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"